Scaling computation for AR and NAR architectures   
In Figures REF  and REF , we present extended results for Section REF , where we scale model parameters and data.
We observe that using larger models (up to 1.5 Million parameters) enables fitting the training dataset better.
The impact of larger models is especially evident for NAR architectures.
As previously noted, recent NAR-based models [1]}, [2]} used more than 30 layers with over 10 Million parameters to outperform AR architectures on fixed TSP sizes.
We believe that such overparameterized networks are able to memorize all patterns for small TSP training sizes [3]},
but the learnt policy is unable to generalize beyond training graph sizes as NAR decoding does not provide a useful inductive bias for TSP.
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